Detecting specified image identifiers on objects

ABSTRACT

Embodiments of the present application relate to a method, apparatus, and system for detecting a specified image identifier. The method includes retrieving a target image to be detected from a predetermined area, binarizing the target image to be detected to obtain a target binary image to be detected, calibrating connected domains of the target binary image to be detected, successively retrieving image features of candidate connected domains, and comparing the image features corresponding to the candidate connected domains to image features of a standard specified identifier image, wherein the candidate connected domains are determined based at least in part on the calibration of the connected domains, and determining a candidate connected domain as the location of the specified identifier image based at least in part on the comparison of the image features corresponding to the candidate connected domains to image features of the standard specified identifier image.

CROSS REFERENCE TO OTHER APPLICATIONS

This application is a continuation of co-pending U.S. patent applicationSer. No. 15/705,016, entitled DETECTING SPECIFIED IMAGE IDENTIFIERS ONOBJECTS filed Sep. 14, 2017 which is incorporated herein by referencefor all purposes, which is a continuation of U.S. patent applicationSer. No. 14/811,345, entitled DETECTING SPECIFIED IMAGE IDENTIFIERS ONOBJECTS filed Jul. 28, 2015, now U.S. Pat. No. 9,799,119, which isincorporated herein by reference for all purposes, which claims priorityto People's Republic of China Patent Application No. 201410367807.9entitled A METHOD AND DEVICE USED TO DETECT SPECIFIED IDENTIFIER IMAGESIN A PREDETERMINED AREA, filed Jul. 29, 2014 which is incorporatedherein by reference for all purposes.

FIELD OF THE INVENTION

The present application relates to the field of fraud detection. Inparticular, the present application relates to a method, device, andsystem for fraud detection in standard cards.

BACKGROUND OF THE INVENTION

Specified identifiers such as logos are generally recognized accordingto various physical detection mechanisms. Specified identifiers aregenerally used to indicate a certain feature of an object such as acredit card. Identifiers can be used as a reference for recognizingobjects (e.g., identifying an account associated with the object orowner of the object, or identifying a type of object). Objects thatinclude specified identifiers generally have specified identifiers thathave distinct appearances or notable spatial locations, so that theobjects or specified identifiers are more easily identified. Thespecified identifiers can be implemented as an identifier object on theobject such as the card. For bank cards, identifier objects generallyinclude the graphic identifiers and text identifiers of the card issuer.

The recognition of identifier objects can serve as an important basisfor distinguishing the authenticity of the bank card. For example, theidentifier objects can be verified to determine whether the objectcomprising the specified identifiers is genuine or valid.

Identifier objects are generally associated with defined sizespecifications and relative location identifiers. For example, authenticbank cards are generally printed using standard identifier objects.However, because the workmanship of counterfeit bank cards is poor andbecause counterfeiting bank cards (or other objects carrying specifiedidentifiers that can be implemented in the form of identifier objects)is performed at a lower cost, printing controls associated with theprinting of authentic bank cards are generally not stringent.Accordingly, recognition of the identifier objects can be used to obtainthe dimensions and relative positioning of the identifier objects.Comparison of the obtained dimensions and relative positioning of theidentifier objects to a standard bank card can provide an effectivereference for distinguishing authenticity of a bank card.

The recognition of identifier objects can provide reference identifiersfor bank card detection and correction.

Bank cards generally have associated defined uniform style rules foridentifier objects. For example, bank cards can have defined edgeidentifiers. The defined edge identifiers can provide a clear indicationthat the card or other object is a bank card. An image of the card to beanalyzed can be captured. An edge can be obtained based on recognitionof the defined edge identifier. The obtained edge can be used to obtainthe currently captured image via an affine transformation based on theobtained edge, thus enabling correction of the image to benefitsubsequent precision detection and positioning of the bank card. Aconventional bank card according to some related art may not beassociated with unified standard. However, a conventional bank card cancomprise markers that generally have clear dimensions and relativeposition. Accordingly, edge information of such bank cards is typicallyrelatively clear. An identifier can be obtained via an affinetransformation using an affine transformation matrix. According to somerelated art, the identifier would have the specifications of the marker.Because bank cards and markers according to some related art arecoplanar, the same affine transformation matrix can be used to obtainbank card numbers affine transformation matrix, to obtain a correctedimage, and to help with detection and localization.

The recognition of identifier objects can enable service providers toengage in accurate advertising push services based on specifiedidentifier objects.

Identifier objects often indicate specified identifiers of a bank card,for example, the card issuer and the scope of use. Accordingly, therecognition of identifier objects enables businesses to engage inaccurate advertising push services targeting the bank card, andincreases service quality. In addition, in the event that an identifierof the identifier object is retrieved, and is combined with augmentedreality technology, multimedia advertising displays can be implemented.

In a given image or video segment, object detection can include findingthe location of a specified identifier or an object of interest in theimage or video. For example, according to specific use scenarios, thefinal output of object detection systems is generally an enclosingrectangular frame or the precise contour of the object.

According to some related art, two categories of object detection aregenerally used. The two categories of object detection include objectdetection using a sliding window and object detection using ageneralized Hough transform. The sliding window object detection methodincludes the use of a trained model to perform a sliding scan ofmultiple dimensions and multiple visual angles of an input image, tofind the enclosing window of a target object by determining the maximumcorresponding positions.

According to the sliding window method implemented by related art forobject detection, when data is being analyzed and scanned, thecomputation volume is significant, and calculation complexity is alsorelatively high, which result in longer detection times that are bettersuited for more complex object structures. Further, the sliding windowmethod is generally not suitable for recognizing an identifier that hasa relatively fixed structure.

BRIEF DESCRIPTION OF THE DRAWINGS

Various embodiments of the invention are disclosed in the followingdetailed description and the accompanying drawings.

FIG. 1 is a flow chart of a method to detect specified identifier imagesin a predetermined area according to various embodiments of the presentapplication.

FIG. 2 is a schematic diagram of a virtual frame computation in a methodto detect specified identifier images in a predetermined area accordingto various embodiments of the present application.

FIG. 3 is a schematic diagram of a virtual edge in a method to detectspecified identifier images in a predetermined area according to variousembodiments of the present application.

FIG. 4 is a schematic diagram of an interception of an area rangeincluding a specified identifier image in a method to detect specifiedidentifier images in a predetermined area according to variousembodiments of the present application.

FIG. 5 is a flow chart of an interception of an area range including aspecified identifier image in a method to detect specified identifierimages in a predetermined area according to various embodiments of thepresent application.

FIG. 6 is a schematic diagram of a binary image after binarization in amethod to detect specified identifier images in a predetermined areaaccording to various embodiments of the present application.

FIG. 7 is a flow chart of the first screening of the candidate connecteddomain in a method to detect specified identifier images in apredetermined area according to various embodiments of the presentapplication.

FIG. 8 is a flow chart of a second screening of a candidate connecteddomain in a method to detect specified identifier images in apredetermined area according to various embodiments of the presentapplication.

FIG. 9 is a schematic diagram of a detection of an upper placement and alower placement of a banking syndicate identifier in a method to detectspecified identifier images in a predetermined area according to variousembodiments of the present application.

FIG. 10 is a structural block diagram of a device configured to detectspecified identifier images in a predetermined area according to variousembodiments of the present application.

FIG. 11 is a functional diagram of a computer system for detecting aspecified identifier according to various embodiments of the presentapplication.

DETAILED DESCRIPTION

The invention can be implemented in numerous ways, including as aprocess; an apparatus; a system; a composition of matter; a computerprogram product embodied on a computer readable storage medium; and/or aprocessor, such as a processor configured to execute instructions storedon and/or provided by a memory coupled to the processor. In thisspecification, these implementations, or any other form that theinvention may take, may be referred to as techniques. In general, theorder of the steps of disclosed processes may be altered within thescope of the invention. Unless stated otherwise, a component such as aprocessor or a memory described as being configured to perform a taskmay be implemented as a general component that is temporarily configuredto perform the task at a given time or a specific component that ismanufactured to perform the task. As used herein, the term ‘processor’refers to one or more devices, circuits, and/or processing coresconfigured to process data, such as computer program instructions.

A detailed description of one or more embodiments of the invention isprovided below along with accompanying figures that illustrate theprinciples of the invention. The invention is described in connectionwith such embodiments, but the invention is not limited to anyembodiment. The scope of the invention is limited only by the claims andthe invention encompasses numerous alternatives, modifications andequivalents. Numerous specific details are set forth in the followingdescription in order to provide a thorough understanding of theinvention. These details are provided for the purpose of example and theinvention may be practiced according to the claims without some or allof these specific details. For the purpose of clarity, technicalmaterial that is known in the technical fields related to the inventionhas not been described in detail so that the invention is notunnecessarily obscured.

The description below expounds many concrete details in order that thepresent application may be fully understood. However, the presentapplication can be implemented in many ways other than those describedhere. A person skilled in the art may extend it similarly withoutviolating the meaning of the present application. Therefore, the presentapplication is not limited by the specific embodiments disclosed below.

Various embodiments of the present disclosure include a method, device,and system for detecting an identifier (e.g., a specific identifier)included on an object. The object including the identifier can be a bankcard, a document, an identification card such as a license or apassport, the like, or any combination thereof. For example, theidentifier can be included on an object in connection with anauthentication mechanism (e.g., the identifiers can be detected inconnection with authenticating the object including the identifier).

The present application uses the area in which the card face of a bankcard is located as the predetermined area to describe the specificprocess of implementing the present application. However, it should benoted that the method according to various embodiments of the presentapplication is not limited to recognition of banking syndicate logos onbank cards. According to various embodiments, the method of the presentapplication can be used in implementations for recognizing oridentifying at least a portion of an object in which specified or knownidentifiers are included in a predetermined area.

In some embodiments, specific identifiers (e.g., known identifiers thatcan be used in connection with authenticating an object) can includeproduct identifiers, corporate logos, the like, or any combinationthereof.

In some embodiments, the predetermined area of a product identifier canbe the surface area of the product itself. In some embodiments, thepredetermined area of a corporate logo can be any carrier (e.g., object)that includes the logo.

FIG. 1 is a flow chart of a method to detect specified identifier imagesin a predetermined area according to various embodiments of the presentapplication.

Referring to FIG. 1, process 100 to detect or identify specifiedidentifiers such as specified identifier images is provided. Process 100can be implemented by device 1000 of FIG. 10 or computer system 1100 ofFIG. 11.

At 110, an image is retrieved from a predetermined area. For example, animage included on an object such as a bank card, a document, or the likecan be retrieved. The image of the object can be captured using acamera, a scanner, or the like. The image from the predetermined areacan be extracted from an image or other electronic representation (e.g.,a file) corresponding to the object. For example, an image or otherelectronic representation corresponding to the object can be processedto retrieve (e.g., extract) the image from the predetermined area of theimage or other electronic representation corresponding to the object.The image from the predetermined area can serve as the target image tobe detected.

At 120, the target image to be detected is binarized. Binarization ofthe target image to be detected includes determining a binaryrepresentation of the target image to be detected (e.g., the image fromthe predetermined area). For example, the target image to be detectedcan be converted to a binary representation thereof. Binarization of thetarget image includes obtaining a binary image (that is, an imagecomprising black or white pixels) and a negative image of the binaryimage. The binary image associated with the target image to be detectedand the negative image of the binary image can be collectively referredto as the target binary images to be detected.

At 130, connected domains of the target binary images to be detected arecalibrated. 130 can further include determining a candidate connecteddomain area based at least in part on the calibration of the connecteddomains of the target binary images to be detected. For example, an areain which the connected domain of the calibrated connected domains has anumber of pixels that satisfies a set threshold can be used as, orotherwise determined to be or set as, a candidate connected domain area.One or more candidate connected domain areas can be successivelydetermined according to each calibrated connected domain that satisfiesthe condition associated with the number of pixels. For example, thecalibrated connected domains can be successively analyzed to determinewhether the corresponding calibrated connected domain is a candidateconnected domain area.

At 140, image features of a candidate connected domain area areretrieved and a location of the specified identifier image isdetermined. In some embodiments, in the event that a plurality ofcandidate connected domain areas is determined, the candidate connecteddomain areas are successively retrieved and compared to a standardspecified identifier image. The comparing of a candidate connecteddomain area can include a comparison of the image features of thecandidate connected domain area against corresponding image features ofthe standard specified identifier image. The candidate connected domainarea having the same or the closest comparison results can be determinedto be the location of the specified identifier image. For example, thecandidate connected domain area that is most similar to the standardspecified identifier image is determined to correspond to the locationto the specified identifier image. In some embodiments, the standardspecified identifier image includes a representation of an identifierthat is used as a basis for determining authenticity of an objectincluding a copy of the specified identifier image.

Through 110-140, when data is being scanned and analyzed, the scanningand analysis of the data is carried out in a relatively small area.Therefore, process 100 is able to reduce the volume of computations anddecrease computation complexity, enabling rapid completion of detectionbased on image features.

Referring to 100, the predetermined area can correspond to the card facearea of a bank card. According to such an implementation, retrieval ofthe image from a predetermined area can include retrieval of a bank cardface image or an image containing the bank card face area. The retrievalof the image can be performed by using a mobile terminal (e.g.,capturing the image using a camera, a scanner, or other image detector)or by downloading an image file from a specific location. In someembodiments, the retrieval of a predetermined area or image containingthis retrieval area can be performed using the image-capturing functionof a mobile terminal such a mobile phone, a tablet, a laptop, or thelike. For example, an application installed on the mobile terminal canbe executed that can cause a camera (e.g., integrated with the mobileterminal) to capture an image of the predetermined area. Thepredetermined area can be selected to reduce the detection scanningrange, thereby increasing detection speed while also lowering the volumeof computations.

According to some embodiments, in order to further increase detectionefficiency and decrease detection of non-banking syndicate identifierareas, during retrieval of the bank card image or image containing thebank card area, preview images of the retrieved bank card image or imagecontaining the bank card face can be retrieved. Based on the dimensionsof the standard bank card and the resolution rate of the preview images,the length and width of a virtual frame of the framed preview images canbe obtained (e.g., determined). The top, bottom, left, and right bordersof the virtual frame can be computed (e.g., determined) based on thelength of the virtual frame. The dimensions of the standard bank cardcan be preset (e.g., according to defined specifications associated witha standard bank card), can be manually entered (e.g., via input to theterminal), or can be determined using the preview image.

FIG. 2 is a schematic diagram of a virtual frame computation in a methodto detect specified identifier images in a predetermined area accordingto various embodiments of the present application.

Referring to FIG. 2, a preview image 200 from which a virtual frame 210is to be computed is provided. In some embodiments, virtual frame 210computed using the preview image 200 can be used in connection withdetecting specified identifier images. Preview image 200 can be capturedin connection with process 100 of FIG. 1. Preview image 200 can becaptured, or otherwise computed, by device 1000 of FIG. 10 or computersystem 1100 of FIG. 11.

Standard bank card dimension parameters can be used in connection withdetecting specified identifier images in a predetermined area. In someembodiments, the standard bank card dimension parameters are specifiedaccording to industry standards or regulations. As an example, thestandard bank card dimension parameters can be determined based at leastin part on volume 3, “Card Bin Number and Identifier Rules,” of the“Bank Card Service Operation Rules.” For example, the card width can be85.60 mm ±0.30 mm, and the card height can be 53.98 mm±0.30 mm. In someimplementations, the error range (tolerances) of 0.30 mm can be ignored(e.g., not considered in connection with the detection of specifiedidentifier images). For example, the card width can be set asCardWidthGdt=85.60 mm, and the card height can be set asCardHeightGdt=53.98 mm. Assuming that the resolution of the mobileterminal screen corresponds to length×height (e.g.,ScreenWidth×ScreenHeight of FIG. 2), the resolution parameter of thepreview image corresponds to width×height (i.e.,PreviewWidth×PreviewHeight of FIG. 2), and the ratio parameter of thevirtual frame area to the preview image area is DisplayRatio, then whencomputing the virtual frame, first, a determination is made as towhether PreviewHeight (e.g., the height of the previewimage)×CardWidthGdt (e.g., the width of the card) is greater thanPreviewWidth (e.g., the width of the preview image)×CardHeightGdt (e.g.,the height of the card).

If PreviewHeight×CardWidthGdt is greater thanPreviewWidth×CardheightGdt, then the virtual frame is scaled such that:

ScaledWidth=CardWidthGdt×PreviewHeight/CardHeightGdt×DisplayRatio, and

ScaledHeight=PreviewHeight×DisplayRatio.

If PreviewHeight×CardWidthGdt is not greater thanPreviewWidth×CardheightGdt, then the virtual frame is scaled such that:

ScaledHeight=CardHeightGdt×PreviewWidth/CardWidthGdt×DisplayRatio, and

ScaledWidth=PreviewWidth×DisplayRatio.

After obtaining (e.g., computing) the width and height of the virtualframe, the top, bottom, left, and right borders of the virtual frame canbe determined based on the resolution parameter of the mobile terminalscreen.

The top border of the virtual frame can be determined according to:

Top=ScreenHeight/2-ScaledHeight/2.

The bottom border of the virtual frame can be determined according to:

Bottom=ScreenHeight/2+ScaledHeight/2.

The left border of the virtual frame can be determined according to:

Left=ScreenWidth/2-ScaledWidth/2.

The right border of the virtual frame can be determined according to:

Right=ScreenWidth/2+ScaledWidth/2.

FIG. 3 is a schematic diagram of a virtual edge in a method to detectspecified identifier images in a predetermined area according to variousembodiments of the present application.

Referring to FIG. 3, a screen 300 of a terminal displaying an imageassociated with a bank card is provided. Screen 300 displays a virtualframe 310. Virtual frame 310 can be displayed in connection with process100 of FIG. 1. Virtual frame 310 can be displayed by device 1000 of FIG.10 or computer system 1100 of FIG. 11.

In some embodiments, the virtual frame 310 covers the upper portion ofthe bank card 320 in the bank card image or area including the bank cardimage. The area within the range covered by the virtual frame 310 isused as the target image to be detected, thus achieving reduction in thedetection range. The area range including the bank identifier imagewithin the virtual frame coverage range can be intercepted (e.g.,captured or processed.) For example, the area range including the bankidentifier image can be intercepted based on specified locationinformation for the banking syndicate identifier in standard bank cards.The area range including the bank identifier image (e.g., theintercepted area range) can be used as the target image to be detected.Accordingly, the detection range can be further reduced (e.g.,minimized) in order to increase detection speed and reduce the volume ofoperations.

FIG. 4 is a schematic diagram of an interception of an area rangeincluding a specified identifier image in a method to detect specifiedidentifier images in a predetermined area according to variousembodiments of the present application.

Referring to FIG. 4, a process 400 of intercepting an area range on abank card 410 is provided. Process 400 can be performed in connectionwith process 100 of FIG. 1. Process 400 can be implemented by device1000 of FIG. 10 or computer system 1100 of FIG. 11.

An area range including a specified identifier image can be interceptedin connection with a method used to detect specified identifier imagesin a predetermined area 410. The predetermined area can be a frontsurface of a bank card. The location of a specified identifier image canbe known in advance (e.g., the location of the specified identifierimage can be predefined). For example, based on volume 3, “Card BinNumber and Identifier Rules,” of the “Bank Card Service OperationRules,” the location information of the banking syndicate identifier onthe bank card can be the location set in advance. As illustrated in FIG.4, the bank identifier area is located on the right side of the card intwo formats: upper placement and lower placement. Predetermined area 410can include the bank identifier area 430 in an upper placement format,and predetermined area 420 can include the bank identifier area 440 in alower placement format.

As illustrated in connection with predetermined area 420 of FIG. 4, inthe event that the banking syndicate identifier is included on thepredetermined area 420 according to the lower placement format, thebanking syndicate identifier 440 is located in the lower right corner ofthe bank card (e.g., of the predetermined area 420). In someembodiments, the specific requirements of the lower placement formatinclude the banking syndicate identifier having a width of 22 mm and aheight of 15 mm, a distance between the right edge of the bankingsyndicate identifier 440 and the right edge of the bank card is 2 mm,and a distance between the bottom edge of the banking syndicateidentifier 440 and the bottom edge of the bank card is 2 mm.

As illustrated in connection with predetermined area 410 of FIG. 4, inthe event that the banking syndicate identifier is included on thepredetermined area 410 according to the upper placement format, thebanking syndicate identifier 430 is located in the upper right corner ofthe bank card. In some embodiments, the specific requirements of theupper placement format include the banking syndicate identifier having awidth of 22 mm and a height of 15 mm, a distance between the right edgeof the banking syndicate identifier and the right edge of the bank cardis 2 mm, and a distance between the top edge of the banking syndicateidentifier and the top edge of the bank card is 2 mm.

Based on the positioning of the banking syndicate identifier describedabove, when the bank card image is intercepted, it is possible to onlyprocess the upper right corner and the lower right corner of the bankcard image or image containing the bank card area. For example, thebanking syndicate identifier can be located and processed by processingthe various locations among which the banking syndicate identifier isknown to exist. As an example, assuming the resolution (width×height) ofthe bank card image or image including the bank card area is:Width×Height, then the images that need to be intercepted (e.g.,captured and processed) are [Width/2, Width]×[0, Height] and [Width/2,Width]×[Height/2, Height].

FIG. 5 is a flow chart of an embodiment of a process for intercepting anarea range including the specified identifier image. Process 500 can beimplemented in connection with process 100 of FIG. 1. Process 500 can beimplemented by device 1000 of FIG. 10 or computer system 1100 of FIG.11.

At 510, a standard image template is established. For example, astandard image template corresponding to a predetermined area isdetermined. In some embodiments, the standard image template is storedas an image file. The standard image template can be defined accordingto regulations (e.g., China UnionPay (CUP) card regulations) promulgatedby a standard unified organization (e.g., CUP). The standard imagetemplate corresponding to the predetermined area can be determined inconnection with a detection of a specified identifier image in thepredetermined area. In some embodiments, the predetermined area is abank card image (e.g., a captured image of a face of a bank card such asthe front of the bank card). Accordingly, a standard image template ofthe bank card image is established.

At 520, location information of a specific identifier is retrieved basedat least in part on the standard image template. In some embodiments,the standard image template based on the bank card image or an imageincluding the bank card area is traversed to retrieve the locationinformation of the banking syndicate image (e.g., the specificidentifier). The standard image template based on the bank card image orthe image including the bank card area can be traversed according tocorrespondences between the standard image template and the bank cardimage or image including the bank card area. In response to traversal ofthe standard image template, the location information associated withthe banking syndicate identifier image in the bank card image or theimage including the bank card area is retrieved.

At 530, an area range including the specific identifier is intercepted.The area range can be intercepted according to the retrieved locationinformation of the specific identifier. For example, based at least inpart on the location information of the specific identifier, the arearange including the banking syndicate identifier can be intercepted andthe area range of this intercepted banking syndicate identifier imagecan be used as the target image to be detected. In some embodiments, theinterception (e.g., processing) of the area range includes identifyingthe area range. The interception of the area range can also includeprocessing the area range (e.g., image processing the area range inconnection with detection of an identifier). The interception of thearea range can include extracting the area range from a larger area ofthe predetermined area.

In some embodiments, based on this bank card image or image includingthe bank card area, the image area including the banking syndicateidentifier can be directly intercepted (e.g., processed) based on thespecified location information of the banking syndicate identifier orbased on the standard image template of the bank card. For example, theimage area including the banking syndicate identifier can be processedaccording to specified location information defined by the standardimage template. The image area (e.g., the intercepted image area) can beused as the target image to be detected. In some embodiments, based onthe virtual frame, only the portion of the virtual frame range thatincludes the banking syndicate identifier image is intercepted, and theimage information outside the virtual frame is discarded, therebyincreasing detection efficiency.

Referring back to 120 of FIG. 1, the target image to be detected isbinarized. Binarization of the target image to be detected is performedto obtain a binary image and a negative image of the binary image. Thebinary image associated with the target image to be detected and thenegative image of the binary image can be collectively referred to asthe target binary images to be detected.

FIG. 6 is a schematic diagram of a binary image after binarization in amethod to detect specified identifier images in a predetermined areaaccording to various embodiments of the present application.

Referring to FIG. 6, process 600 to binarize a target image is provided.Process 600 can be implemented in connection with process 100 of FIG. 1.For example, process 600 can be implemented in connection with 120 ofFIG. 1. Process 600 can be implemented by device 1000 of FIG. 10 orcomputer system 1100 of FIG. 11.

The target image to be detected (e.g., the intercepted image of the arearange of the image including the banking syndicate identifier) isbinarized. Image binarization refers to setting the grayscale values ofthe pixel points on the image to 0 or 255. In other words, the targetimage as a whole is caused to exhibit the visual effect of includingonly black and white.

In some embodiments, the target image is caused to exhibit the visualeffect of including only black and white by taking a grayscale image 610that has 256 brightness levels and, through the selection of appropriatethreshold values, obtaining a binary image 620 that still reflects theoverall and local features of the image. For example, although thebinary image 620 includes less pixel variation than grayscale image 610,the binary image 620 conveys the features of the target imagesufficiently to be readable by a human or a computer. The binary image620 can sufficiently convey a specified identifier included therein.

In order to process and analyze a binary image, the original image mustfirst be converted into grayscale. The grayscale image is binarized toobtain a binary image. The binary image can be used for furtherprocessing of the image. For example, the binary image corresponding toan image can be more simply processed than the image or the grayscaleimage corresponding to the image. For example, the set properties of theimage are only concerned with the locations of points having pixelvalues of 0 or 255 (or 0 or 1 in some implementations,) multiple pixelvalues are no longer involved, which simplifies processing of the image,and thus reduces the volume of data processed and compressed.

In some embodiments, threshold values can be used in connection withbinarizing an image. For example, in the binarization process 600 (e.g.,120 of FIG. 1), Otsu's method can be employed for the selection ofthreshold values, and the resulting segmenting of the grayscale imageinto two parts, the background part and the target (foreground) part.Otsu's method, also known as the maximum between-class variance method,is a self-adaptive thresholding method that segments the image intobackground and target portions according to the grayscale specificationsof the image. According to Otsu's method, a greater between-classvariance between the background and the target indicates a greaterdifference constituted between the image background and target.Foreground pixel points as a percentage of the image and the averagegrayscale value, and background pixel points as a percentage of theimage and the average grayscale value are used to compute the variancesof the foreground and background images, the corresponding grayscalevalue when the variance is greatest is selected as the global thresholdvalue, and binary conversion of the image is performed based on thisglobal threshold value.

During the process of analyzing the binary image, because the whitepixels (e.g., the target pixels) are marked in the binary image, andafter an image that contains the banking syndicate identifier isbinarized as the target image to be detected, the banking syndicateidentifier may be binarized as foreground (e.g., which has a pixel valueof 1) or background (e.g., which has a pixel value of 0). Accordingly,after the binary image is obtained, a negative 630 of the binary image620 can be generated. Both the binary image 620 and the negative image630 can be collectively used as the target binary images to be detected.

An embodiment of a method of binary image analysis is connected domainmarking, which is the foundation for binary image analysis. By markingthe white pixels (e.g., the target pixels) in the binary image 620, theconnected domain marking method allows each individual connected domainto form an identified block, whereupon the geometric parameters of theidentified blocks can be retrieved. For example, the contour, anenclosing rectangle, a centroid, a geometric invariant, or the like canbe retrieved from the identified block.

The processing associated with connected domain marking of binary imagescorresponds to the extraction of the set of mutually adjacent(4-adjacency or 8-adjacenty) pixels having a pixel value of “1” from adot matrix image that combines white pixels (typically expressed as “1”in binary images) and black pixels (typically expressed as“0”). Binaryimages include two colors, black (which has a pixel value of 0) andwhite (which has a pixel value of 1 or 255), which respectively serve asthe background color and the target color. In some embodiments, themarking algorithm only marks the target pixels.

Referring back to 130 of FIG. 1, the connected domains of the targetbinary images are calibrated. The connected domains of the target binaryimage to be detected are used to determine a candidate connected domain.For example, the calibrated connected domains are used to determine thecandidate connected domains as those connected domains among thecalibrated connected domains having a number of pixels that satisfies aset threshold range.

In some embodiments, connected domain calibration is the basis forbinary image analysis. The processing of connected domain marking ofbinary images includes the extraction of the set of mutually adjacentpixels having a pixel value of “1” from a dot matrix image that combineswhite pixels (typically expressed as “1” in binary images) and blackpixels (typically expressed as “0”). For example, the binary imageincludes the colors of black (pixel value of 0) and white (pixel valueof 1 or 255), which respectively serve as the background color and thetarget color. During connected domain calibration, the calibrationalgorithm only performs calibration on the target pixels (white pixels).

In some embodiments, the calibration of the connected domains usesvarious types (e.g., definitions) of adjacency. For example, two commontypes of adjacency are generally used in connection with calibration ofa connected domain. The two common types of adjacency include a4-adjacency and an 8-adjacency. 4-adjacency has a total of 4 points(e.g., top, bottom, left, and right). 8-adjacency has a total of 8points, including points in diagonal positions (e.g., top, bottom, left,right, upper left, upper right, lower left, and lower right). Setsformed by mutually connected points at the top, bottom, left, and right,or at the top, bottom, left, right, upper left, and upper right, arecalibrated as connected domains.

In some embodiments, after the intercepted area range including thebanking syndicate identifier image has been binarized, the connecteddomain calibration is used on the binary image 620 to calibrate theconnected domains in the binary image 620. For example, the calibrationof connected domains is performed on the binary image 620. In someembodiments, the calibration of connected domains is performed on thebinary image 620 to calibrate all connected domains having a pixel valueof 1. Threshold judgment can be performed with respect to eachindividual calibrated connected domain. If the number of pixels of aconnected domain is within a given threshold range, then the connecteddomain is retained. Otherwise, if the number of pixels of a connecteddomain is not within a given threshold range, connected domaincalibration is performed on the negative image 630 of the target binaryimage to be detected, and threshold determination can be performed withrespect to the number of pixels in the calibrated connected domainscontained in the negative image 630. If the number of pixels is withinthe threshold range, then the connected domain is retained. If thenumber of pixels is not within the threshold range, then the connecteddomain is discarded.

In some embodiments, the number of pixels of the connected domainsformed by the banking syndicate identifier image will be within areasonable range. The reasonable range can be set as the threshold rangefor the connected domains of the banking syndicate identifier image.

The following may be referred to for the computation of the aforesaidthreshold range of the banking syndicate identifier connected domains:

The threshold range of the banking syndicate identifier of the connecteddomains can be computed according to: lower limit of thresholdcorresponds to LowThreshold=Width×Height×lower limit coefficient; andupper limit of threshold HighThreshold=Width×Height×upper limitcoefficient.

The Width and Height used in connection with computing the thresholdrange corresponds to the respective width and height of the retrievedpredetermined area or image including the predetermined area. Forexample, Width and Height can be the width and height of the targetimage to be detected, and the lower limit coefficient and the upperlimit valuation can be the empirical values of 0.011 and 0.043,respectively.

In the event that the specified identifier information being detected isnot a banking syndicate identifier, a threshold range can be set basedat least in part on the reasonable range into which the number of pixelsof the specified identifier connected domain can fall.

Referring back to 140 of FIG. 1, image features of the candidate domainareas are retrieved and a location of the specified identifier isdetermined. In some embodiments, the image features of the candidateconnected domains are retrieved, and the image features of the candidateconnected domains are compared to the corresponding image features ofthe standard specified identifier image. Based on the comparison of theimage features of the candidate connected domain to a correspondingimage feature of the standard specified identifier image, the candidateconnected domain is determined. For example, a location of the specifiedidentifier image can correspond to the area having an identical or theclosest comparison results as the location of the specified identifierimage. For example, the candidate connected domain area that is mostsimilar to the standard specified identifier image is determined tocorrespond to the location to the specified identifier image.

In some embodiments, the determining of the location of the specifiedidentifier image can include the image features of the candidate bankingsyndicate identifier connected domain area and the standard bankingsyndicate identifier image, and comparing the corresponding imagefeatures of the candidate banking syndicate identifier connected domainarea and the standard banking syndicate identifier image. In someembodiments, the comparing of the corresponding image features of thecandidate banking syndicate identifier connected domain area and thestandard banking syndicate identifier image includes using a singleimage feature comparison or a combined image feature comparison. In someembodiments, image features include the volume ratio of the connecteddomain, the major axis direction of the circumscribed ellipse of theconnected domain, the outer contour shape of the connected domain, orthe aspect ratio of the enclosing rectangle of the connected domain. Insome embodiments, the image features correspond to image shape features.The image shape features can further include distance features, imageconvex and concave features, length features, or the like.

The volume ratio of the connected domain and the major axis direction ofthe circumscribed ellipse of the connected domain can be used as thefirst set of combined image features for the screening of the candidateconnected domain areas of the banking syndicate identifier image.

FIG. 7 is a flow chart of the first screening of the candidate connecteddomain in a method to detect specified identifier images in apredetermined area according to various embodiments of the presentapplication.

Referring to FIG. 7, process 700 to screen a candidate connected domainis provided. Process 700 can be implemented in connection with process100 of FIG. 1. For example, process 700 can be implemented in connectionwith 140 of FIG. 1. Process 700 can be implemented by device 1000 ofFIG. 10 or computer system 1100 of FIG. 11.

At 710, an enclosing frame of the candidate connected domain area isretrieved. The enclosing frame can correspond to an area of pixels thatencloses the specified identifier image. For example, in animplementation in which the specified identifier image is a bankingsyndicate identifier, the enclosing frame can correspond to an areaenclosing the banking syndicate identifier.

In some embodiments, the enclosing frame of the candidate connecteddomain area is retrieved using a scanning of a candidate bankingsyndicate identifier connected domain image. For example, the scanningof the candidate banking syndicate identifier connected domain image caninclude scanning inward from the outermost edge of the candidate bankingsyndicate identifier connected domain image and when the foregroundcolor (white pixels) is detected, scanning is stopped, and an enclosingframe is obtained. The candidate banking syndicate identifier connecteddomain image can be scanned in multiple directions (e.g., horizontal andvertical). The enclosing frame is generally rectangular. Other scanningmethods can be used to retrieve the enclosing frame.

At 720, a volume ratio of foreground pixels is computed. For example,the volume ratio of the foreground color pixels in the candidateconnected domain within the enclosing frame and the area of theenclosing frame is computed. For example, the ratio of foreground colorpixels to background color pixels or the ratio of foreground colorpixels to a total number of pixels in the enclosing frame can becomputed. The volume ratio of the connected domain can be obtained bytabulating the number of foreground color (white) pixels in therectangular enclosing frame, and dividing the number of white pixels bythe area of the rectangular enclosing frame.

At 730, it is determined whether the volume ratio is greater than thethreshold value of the specified identifier image. In some embodiments,it is determined whether the volume ratio is greater than or equal tothe threshold range of the specified identifier image.

In some embodiments, the determination of whether the volume ratio offoreground pixels is greater than the threshold range can be based on athreshold value Tsolid for the volume ratio of the standard bankingsyndicate identifier connected domain. For example, in some embodiments,it can be determined whether the volume ratio of the candidate connecteddomain conforms to the volume ratio image feature of the standardbanking syndicate identifier connected domain. The volume ratio of thecandidate connected domain can conform to the volume ratio image featureof the standard banking syndicate identifier connected domain if thevolumes are the same or within a statistically relevant range. Thethreshold value Tsolid can be obtained using information associated withthe standard banking syndicate identifier image. For example, thethreshold value Tsolid can be obtained by retrieving the standardbanking syndicate identifier image from all angles, computing a volumeratio of the standard banking syndicate identifier image with theexternal matrix, selecting a threshold value Tsolid, and determiningwhether the candidate connected domain is the banking syndicateidentifier image by comparing the volume ratio of the candidate bankingsyndicate identifier connected domain to Tsolid.

In the event that the volume ratio of the candidate banking syndicateidentifier connected domain is not greater than the threshold value(e.g., Tsolid) of the specified identifier range, at 735, the candidatedomain area is discarded.

In the event that the volume ratio of the candidate banking syndicateidentifier connected domain is greater than the threshold value (e.g.,Tsolid), the candidate banking syndicate identifier connected domain isdetermined to correspond to the banking syndicate identifier connecteddomain. Because it is possible for non-banking syndicate identifierconnected domains having feature images that are identical to the volumeratio of the standard banking syndicate identifier connected domain tobe present among the candidate banking syndicate identifier connecteddomains, various embodiments use another image feature (e.g., the imagefeature of the ellipse major axis direction of the connected domain) tofurther confirm the candidate banking syndicate identifier. For example,in response to determining the volume ratio of the candidate bankingsyndicate identifier connected domain is greater than the thresholdvalue (e.g., Tsolid), the candidate banking syndicate identifierconnected domain is further analyzed or processed to confirm thecandidate banking syndicate identifier. For example, the major axis ofthe connected domain ellipse of the candidate banking syndicateidentifier connected domain can be analyzed or processed.

In the event that the volume ratio of the candidate banking syndicateidentifier connected domain is greater than the threshold value (e.g.,Tsolid), at 740, the candidate connected domain area is filled (e.g.,filling a domain that defines an empty place such as a circle). In someembodiments, the foreground color (white) of the banking syndicateidentifier candidate connected domain area is filled.

At 750, a direction of the filled candidate connected domain area iscomputed. For example, the direction of the filled banking syndicateidentifier candidate connected domain area is determined.

In some embodiments, the direction of the filled banking syndicateidentifier candidate connected domain area is computed using ellipsefitting. For example, the major axis direction of a circumscribedellipse on the banking syndicate identifier candidate connected domainis used as the direction of the banking syndicate identifier candidateconnected domain. In some embodiments, the ellipse fitting includesperforming edge detection of the banking syndicate identifier candidateconnected domain, recording points along the detected edge, andperforming ellipse fitting of the points along the detected edge.

After the ellipse fitting major axis direction has been computed,coordinate axes are established using the lower left corner of thebanking syndicate identifier candidate connected domain as the basepoint. The X-axis of the established coordinate axes is defined as thereference axis, and an angle of intersection is formed between the majoraxis of the ellipse and the X-axis. Similarly, for the standard bankingsyndicate identifier image, the angle of intersection formed between theellipse major axis and the X-axis can also be computed.

At 760, it is determined whether the angle of intersection correspondingto the banking syndicate identifier candidate connected domain is withina predetermined threshold range (e.g., value) of the angle ofintersection of the standard banking syndicate identifier image. Thethreshold range of 760 can be the same as or different from thethreshold range of 730.

In some embodiments, the threshold range for the angle of intersectioncan be based on the elliptical fitting of the standard banking syndicateidentifier image and computation of the angle of intersection of themajor axis direction of this ellipse and the reference axis. In theretrieval of the banking syndicate identifier target image to bedetected, because the effects of factors including the retrieval anglemay be present, which can also affect the angle of the candidateconnected domain, various embodiments set a threshold range for theangle of intersection based on the angle of intersection of the majoraxis of the ellipse and the reference axis in the standard bankingsyndicate identifier in order to prevent a determination of whether thebanking syndicate identifier target image to be detected corresponds tothe standard syndicate identifier target image from being affected bythe adverse factors affecting the retrieval angle (e.g., errors inherentin the elliptical fitting of the standard banking syndicate identifier).The reference axis can be the X-axis. The above reference axis can beset based on requirements of the determination (e.g., it can also be theY-axis, if the requirements of the determination associated with themajor axis direction of the circumscribed ellipse of the connecteddomain can be satisfied).

In the event that the angle of intersection corresponding to the bankingsyndicate identifier candidate connected domain is not within thepredetermined threshold value of the angle of intersection of thestandard banking syndicate identifier image, at 765, the candidatedomain area is discarded.

In the event that the angle of intersection corresponding to the bankingsyndicate identifier candidate connected domain is within thepredetermined threshold value of the angle of intersection of thestandard banking syndicate identifier image, at 770, the bankingsyndicate identifier candidate connected domain is determined to be(e.g., regarded as) the area in which the banking syndicate identifierimage is located.

It should be noted that when the specified identifier has two (defined)enclosing edges (e.g., inner layer edge and outer layer edge connecteddomains), a second volume ratio determination can be made with respectto the banking syndicate identifier candidate connected domain image.However, the threshold value used during this second volumedetermination is not the Tsolid described above, but rather anotherthreshold value Tsolid' set for the purposes of this second volumedetermination. The second volume determination can be made usingvariations of 710-770. The pixel value of the inner layer connecteddomain is different from the pixel value of the outer layer connecteddomain, and therefore a different threshold value is employed.

Process 700 is described in connection with a banking syndicateidentifier candidate connected domain image. According to variousembodiments, process 700 can be performed in connection with otheridentifier connected domain images. For example, process 700 can beperformed in connection with a specified identifier other than a bankingsyndicate identifier.

Process 700 can be used to confirm that the banking syndicate identifiercandidate connected domain image corresponds to the banking syndicateidentifier image. In some embodiments, further confirmation of thebanking syndicate identifier candidate connected domain image can beperformed. For example, a second screening of the candidate connecteddomain area can be performed using the outer contour shape of theconnected domain as the single condition, based on a determination thatthe angle of intersection of the ellipse major axis and the referenceaxis is within a predetermined threshold range.

FIG. 8 is a flow chart of a second screening of a candidate connecteddomain in a method to detect specified identifier images in apredetermined area according to various embodiments of the presentapplication.

Referring to FIG. 8, process 800 to screen a candidate connected domainis provided. Process 800 can be implemented in connection with process100 of FIG. 1. For example, process 800 can be implemented in connectionwith 140 of FIG. 1. Process 800 can be implemented by device 1000 ofFIG. 10 or computer system 1100 of FIG. 11.

At 810, a filled image is obtained. In some embodiments, the filledimage is obtained based on filling the candidate connected domain area.The candidate connected domain area can be filled similar to the fillingof the candidate connected domain at 740 of FIG. 7.

At 820, an edge contour image is obtained. In some embodiments, the edgecontour image is obtained based at least in part on detection of theedges of the filled image.

In some embodiments, after the candidate connected domain area is filledwith white, the white area corresponds to the candidate connected domainarea, and the edge contour image of the filled image is obtained byperforming an edge detection. The edge detection can correspond to atechnique that captures important features of objects in images commonlyused in image processing. In various embodiments, edge detectionincludes differential edge detection, gradient edge detection, theRoberts edge detection operator, the Sobel edge detection operator, thePrewitt edge detection operator, and the Laplace edge detectionoperator, or the like. In some embodiments, the Sobel edge detectionoperator is used to detect the edges of the filled image. Using theSobel edge detection operator, edges are detected primarily when extremaare reached in the edge area, based on the grayscale weighted differenceof the adjacent pixel points above, below, to the left, and to the rightof the pixel point; this has the effect of smoothing noise, enablingprovision of more precise edge directional information. There are twoSobel operators, one for detecting horizontal edges, and the other fordetecting vertical edges. In contrast to the Prewitt operator, theimpact of pixel location is weighted in the Sobel operators, which canreduce the degree of edge blur and produce better results. According tovarying requirements of various embodiments, other edge detectionoperators can be used to detect the edges of the filled image.

At 830, sampling pixel points at edges of the edge contour image areretrieved. For example, separate retrieval of sampling pixel points forthe top and bottom edges and left and right edges of the edge contourimage is performed. Sampling pixel points for the top and bottom edgesand sampling pixel points for the left and right edges are selected suchthat the selected sampling pixel points are within a set thresholdrange.

In some embodiments, retrieval of the sampling pixel points at edges ofthe edge contour image includes scanning the edge contour image. Forexample, the edge contour image is scanned to sample the sampling pixelpoints for the top and bottom edges and the left and right edges in theedge contour image. In the event that the specified identifiercorresponds to a banking syndicate identifier, the edge contour of thefilled image can be a rectangular image, the top and bottom edges andthe left and right edges of the rectangular image are separatelyscanned, and the same scanning process can be used for scanning the topand bottom edges and the left and right edges.

The retrieval of sampling pixel points can include sequentially scanningedges of the rectangular image and selecting pixel points having twopoints of intersection between the scanning line and the edge image. Thedistances between the selected pixel points can be computed and (all)the maximum values maxDist between the pixel points are recorded andused as the sampling pixel points. Because the rectangular image caninclude jagged edges in the edges of the rectangular image, a maximumdistance threshold range is set for the selection of sampling pixelpoints. The threshold range can be set at maxDist±maxDist×5%. Pixelpoints within this threshold range are selected as the sampling pixelpoints, and (all) pixel points outside this threshold range arediscarded. Accordingly, sampling pixel points for the top and bottomedges of the rectangular image can be obtained. In various embodiments,sampling pixel points for the left and right edges can be obtained inthe same manner.

At 840, line segment combination information of the edge contour imageis obtained. In some embodiments, the line segment combinationinformation of the edge contour image is obtained by fitting theselected sampling pixel points.

The selected sampling pixel points can be fitted according to polynomialcurve fitting. For example, the sampling pixel points of the four edgesof the candidate connected domain rectangular image selected at 830 canbe fitted using polynomial curve fitting methods to obtain four linesegments. The four line segments obtained by fitting the sampling pixelpoints can be combined to obtain four vertices. According to variousimplementations, different fitting methods or computation methods can beused to perform fitting of the sampling pixel points according to thespecific identifier to be detected.

At 850, it is determined whether the segment combination informationmatches the segment combination information of the standard specifiedidentifier image. For example, it is determined whether the line segmentcombination information of the candidate connected domain area isidentical to the line segment combination information of the standardspecified identifier image.

The determination of whether the line segment combination information ofthe candidate connected domain area matches the line segment combinationinformation of the standard specified identifier image can includeregarding the line segment combination information obtained at 840(e.g., the four line segments and four vertices obtained by combiningthe four line segments obtained at 840), and comparing the four linesegments and four vertices to the image of the banking syndicateidentifier of the standard bank card. If the four line segments and fourvertices are identical (or within a statistically relevant similaritythreshold) to the banking syndicate identifier, then the bankingsyndicate identifier candidate connected domain is deemed to be thebanking syndicate identifier image.

In the event that the segment combination information does not match thesegment combination information of the standard specified identifierimage, at 855, the candidate domain area is discarded. In someembodiments, an indication that the specified identifier image area hasnot been detected can be output. The indication can indicate a failureof the detection of the specified identifier image area.

In the event that the segment combination information matches thesegment combination information of the standard specified identifierimage, at 860, the candidate domain area is determined to correspond tothe specified identifier image.

In the event that the candidate banking syndicate identifier isdetected, the system can output the four corners of the bank syndicateindicator. If the combined information obtained through detectionincludes vertex information for more or fewer than 4 vertices, then thecandidate area can be determined to not correspond to the bankingsyndicate logo. If the detected banking syndicate identifier candidatearea is empty, detection of the banking syndicate identifier fails. Theuser can be provided with an indication that notifies of the failure todetect the banking syndicate identifier. In some embodiments, if thenumber of detected banking syndicate identifier areas is greater thanone, then a user can be prompted to select the banking syndicateidentifier among a set of the detected banking syndicate identifierareas. For example, all candidate areas can be provided to the user, andthe user can input a selection of a banking syndicate identifier areaautonomously.

FIG. 9 is a diagram illustrating a detection of an upper placement and alower placement of a banking syndicate identifier in a method to detectspecified identifier images in a predetermined area according to variousembodiments of the present application.

Referring to FIG. 9, examples of bank cards 900 including bankingsyndicate identifiers are provided. Bank card 910 includes a bankingsyndicate identifier 915 placed according to a lower placement format.Bank card 920 includes a banking syndicate identifier 925 placedaccording to an upper placement format. Banking syndicate identifier 915or banking syndicate identifier 925 can be detected in connection withprocess 100 of FIG. 1, process 500 of FIG. 5, process 700 of FIG. 7, orprocess 800 of FIG. 8. Banking syndicate identifier 915 or bankingsyndicate identifier 925 can be detected by device 1000 of FIG. 10 orcomputer system 1100 of FIG. 11.

The above is a description of specified identifier detection using theexample of banking syndicate identifiers. In some embodiments, thebanking syndicate identifiers include various image features. The imagefeatures can include shape features, including such properties as aparallelogram and four vertices. If the specified identifier is text, aproduct identifier, or a corporate logo, the specified identifier can bedetected using the image features according to various embodiments ofthe present disclosure.

For example, the logos of the various issuing banks are described usingthe example of the China Merchants Bank logo. Refer to FIG. 3, region[number and label it in figure] the image features of the ChinaMerchants Bank logo image include circular, the letter M and the variousvertices of the letter M, triangular, or convex-concave properties, orthe like, and the various detection methods according to someembodiments can be used to perform detection of the logo.

After the banking syndicate identifier has been detected, the affinetransformation matrix of the banking syndicate identifier image can becomputed based on information concerning the four vertices of thebanking syndicate identifier and the vertex information of the standardbanking syndicate information; other information on the bank card can becomputed based on the affine transformation matrix.

Assuming that the coordinates of the four vertices of a standard bankingsyndicate identifier are [x1, y1], [x2, y2], [x3, y3], and [x4, y4], andthe coordinates of the four vertices in the detected candidate connecteddomain image are [X1, Y1], [X2, Y2], [X3, Y3], and [X4, Y4], in someembodiments, based on correspondences between the four points, theaffine transformation (projective) matrix M can be computed, and anaffine transformed image can be computed for each pixel point of thedetected image. The visual effect in which this image is located is apositive viewing angle image. Based on specifications associated withthe bank card, any information about the bank card can be obtained. Forexample, if information associated with the card number on the bank cardneeds to be detected, the pixel points of the card number can bemultiplied by matrix M to obtain a positive viewing angle image of thecard number, and thereby obtain accurate card number information.

In the event that information on the object (e.g., bank card) isdetected, or the specified identifier is detected from the predeterminedarea of the object, the object or the user associated with the objectcan be authenticated. For example, the information detected from theobject or the specified identifier can be used to determine that theobject is authentic (e.g., is not fraudulent) and the user can beauthenticated. The user or the object can be authenticated in connectionwith a login to a web server (e.g., an e-commerce service, or the like)or in connection with an online transaction. In some embodiments, inresponse to identifying (e.g., detecting) the specified identifier(e.g., the banking syndicate identifier), the object (e.g., the bankcard) or the user associated therewith, is authorized to perform atransaction on an e-commerce platform.

FIG. 10 is a structural block diagram of a device configured to detectspecified identifier images in a predetermined area according to variousembodiments of the present application.

Referring to FIG. 10, device 1000 configured to detect a specifiedidentifier image is provided. In some embodiments, device 1000 can be,or otherwise integrated with, a mobile terminal such as a mobile phone,a laptop, a tablet, or the like. Device 1000 can implement process 100of FIG. 1, process 500 of FIG. 5, process 700 of FIG. 7, or process 800of FIG. 8.

Device 1000 includes a retrieval module 1010, a binarization module1020, a connected domain calibration module 1030, and an image featurecomparison module 1040.

The retrieval module 1010 is configured to retrieve an image from thepredetermined area or image including this predetermined area. Theretrieved image can serve as the target image to be detected. Theretrieval module 1010 can retrieve the image from an object such as abank card, a document, or the like. The retrieval module 1010 canretrieve the image by capturing an image of the object. The retrievalmodule 1010 can extract the image from an image or other electronicrepresentation (e.g., a file) corresponding to the object. For example,the retrieval module 1010 can parse an image or other electronicrepresentation corresponding to the object to retrieve the image fromthe predetermined area.

The binarization module 1020 is configured to perform binarization ofthe target image to be detected. The binarization module 1020 obtains abinary image and a negative image of the binary image based on thebinarization of the target image to be detected. For example, thebinarization module 1020 obtains the binary image (and a negative imageof the binary image) by computing a binary representation of the targetimage to be detected. The binary image associated with the target imageto be detected and the negative image of the binary image can becollectively referred to as the target binary image to be detected.

The connected domain calibration module 1030 is configured to calibratethe connected domains of the target binary image to be detected, and touse the connected domains among the calibrated connected domains havinga number of pixels that satisfies the set threshold range as thecandidate connected domains.

The image feature comparison module 1040 can be configured tosuccessively retrieve the image features of the candidate connecteddomains, to compare the image features of the candidate connecteddomains to the corresponding image features of the standard specifiedidentifier image, and to determine a location of the specifiedidentifier image. In some embodiments, the image feature comparisonmodule 1040 determines the location of the specified identifier image byregarding the connected domain having identical or the closestcomparison results as the location of the specified identifier image.

The retrieval module 1010 can include a preview image retrievalsub-module 1011, a computation sub-module 1012, a framing sub-module1013, and an interception sub-module 1014,

The preview image retrieval sub-module 1011 is configured to retrievepreview images of the predetermined area or image including thepredetermined area.

The computation sub-module 1012 is configured to compute the area rangeof the preview image. The computation sub-module 1012 can obtain thevirtual frame parameters for the obtained the preview image based on theactual dimension parameters of the predetermined area and the resolutionparameter of the preview image.

Based on the actual dimensions of the predetermined area or imageincluding the predetermined area, the computation sub-module 1012 candetermine whether the product of multiplying the height of the previewimage by the width of the predetermined area or image including thepredetermined area is greater than the product of multiplying the widthof the preview image by the height of the predetermined area or imageincluding the predetermined area.

If the product of multiplying the height of the preview image by thewidth of the predetermined area or image including the predeterminedarea is greater than the product of multiplying the width of the previewimage by the height of the predetermined area or image including thepredetermined area, then the width of the virtual frame corresponds tothe width of the predetermined area or image including the predeterminedarea multiplied by the height of the preview image divided by the actualheight of the predetermined area or image containing the predeterminedarea multiplied by the ratio of the virtual frame to the preview image;and the height of the virtual frame corresponds to the height of thepreview image multiplied by the ratio of the virtual frame to thepreview image.

If the product of multiplying the height of the preview image by thewidth of the predetermined area or image including the predeterminedarea is not greater than the product of multiplying the width of thepreview image by the height of the predetermined area or image includingthe predetermined area, then the width of the virtual frame correspondsto the width of the preview image multiplied by the ratio of the virtualframe to the preview image; the height of the virtual frame correspondsto the height of the predetermined area or image including thepredetermined area multiplied by the width of the preview image dividedby the width of the predetermined area or image including thepredetermined area multiplied by the ratio of the virtual frame to thepreview image.

In some embodiments, the top border of the virtual frame corresponds toone half of the difference between the height of the display screen andthe height of the virtual frame. In some embodiments, the bottom borderof the virtual frame corresponds to one-half of the sum of the height ofthe display screen and the height of the virtual frame. In someembodiments, the left border of the virtual frame is one half of thedifference between the width of the display screen and the width of thevirtual frame. In some embodiments, the right border of the virtualframe is one-half of the sum of the width of the display screen and thewidth of the virtual frame.

The framing sub-module 1013 is configured to frame the preview image inthe area range, and retrieve the predetermined area or image includingthe predetermined area, and use the image within this framed area rangeas the target image to be detected.

The interception sub-module 1014 is configured to intercept the arearange including the specified identifier image. The interceptionsub-module 1014 can be used based on the location information of thespecified identifier image in the predetermined area or image includingthe predetermined area to intercept the area range containing thespecified identifier image, to serve as the target image to be detected.The specified location information in the interception unit cancorrespond to the location information of the specified identifier imagein the predetermined area or image including the predetermined area setin advance. As an example, in the event that a banking syndicateidentifier on a bank card corresponds to the specified identifier, thestandard bank dimension parameters can be determined based at least inpart on volume 3, “Card Bin Number and Identifier Rules,” of the “BankCard Service Operation Rules.” In some embodiments, the specifiedlocation of the banking syndicate identifier is in the upper right orlower right corner of the bank card face.

In some embodiments, the interception sub-module 1014 can include anestablishing sub-module, a traversal sub-module, and a target image tobe detected determination sub-module.

The establishing sub-module is configured to establish a standard imagetemplate of the predetermined area or image including the predeterminedarea.

The traversal sub-module is configured to traverse the standard imagetemplate based on the predetermined area or image including thepredetermined area and retrieve the location information of thespecified identifier image in the predetermined area or image includingthe predetermined area. The traversal sub-module can retrieve thelocation information according to correspondences between the standardimage template and the predetermined area or image including thepredetermined area.

The target image to be detected determination sub-module is configuredto intercept an image area containing the specified identifier containedin the predetermined area or image containing the predetermined area.For example, the target image to be detected determination sub-modulecan intercept the image area containing the specified identifiercontained in the predetermined area or image containing thepredetermined area based on the location information retrieved by thetraversal sub-module.

In some embodiments, the upper limit value of the threshold range usedby the connected domain calibration module 1030 in connection withdetermining the candidate connected domains is the product ofmultiplying the width, height, and upper limit coefficient of thepredetermined area or image including the predetermined area. In someembodiments, the lower limit value of the threshold range used by theconnected domain calibration module 1030 in connection with determiningthe candidate connected domains is the product of multiplying the width,height, and lower limit coefficient of the predetermined area or imageincluding the predetermined area.

In some embodiments, the image feature comparison module 1040 comprisesa combined image feature screening sub-module or a single image featurescreening sub-module. The image feature comparison module 1040 can use asingle image feature comparison or a combined image feature comparison.In some embodiments, the image features include the volume ratio of theconnected domain, the major axis direction of the circumscribed ellipseof the connected domain, the outer contour of the connected domain, theaspect ratio of the enclosing rectangle of the connected domain, thelike, or any combination thereof.

The combined image feature screening sub-module can include an enclosingframe retrieval sub-module, a volume ratio computation sub-module,volume ratio threshold determining sub-module, a filling computationsub-module, and an angle of intersection determining sub-module. Thecombined image feature screening sub-module can be implemented toperform process 700 of FIG. 7.

The enclosing frame retrieval sub-module can be configured to retrievean enclosing frame of a candidate connected domain area. The enclosingframe can correspond to an area of pixels that encloses the specifiedidentifier image. For example, in an implementation in which thespecified identifier image is a banking syndicate identifier, theenclosing frame can correspond to an area enclosing the bankingsyndicate identifier. The enclosing frame retrieval sub-module can beimplemented to perform 710 of process 700.

In some embodiments, the enclosing frame retrieval sub-module canretrieve the enclosing frame of the candidate connected domain areausing a scanning of a candidate banking syndicate identifier connecteddomain image. For example, the scanning of the candidate bankingsyndicate identifier connected domain image can include scanning inwardfrom the outermost edge of the candidate banking syndicate identifierconnected domain image and when the foreground color (white pixels) isdetected, scanning is stopped, and an enclosing frame is obtained. Thecandidate banking syndicate identifier connected domain image can bescanned in multiple directions (e.g., horizontal and vertical). Theenclosing frame is generally rectangular. Other scanning methods can beused to retrieve the enclosing frame.

The volume ratio computation sub-module is configured to compute avolume ratio of the foreground color pixels. The volume ratiocomputation sub-module is configured to compute a volume ratio of theforeground color pixels within the enclosing frame to the area of theenclosing frame. The volume ratio computation sub-module can compute thevolume ratio of the connected domain by tabulating the number offoreground color (white) pixels in the rectangular enclosing frame, anddividing the number of white pixels by the area of the rectangularenclosing frame. The volume ratio computation sub-module can beimplemented to perform 720 of process 700.

The volume ratio threshold determining sub-module is configured todetermine whether the volume ratio is greater than a threshold rangecorresponding to the specified identifier image. The threshold rangecorresponding to the specified identifier image can be preset. In someembodiments, the determination of whether the volume ratio of foregroundpixels is greater than the threshold range can be based on a thresholdvalue Tsolid for the volume ratio of the standard banking syndicateidentifier connected domain. In the event that the volume ratio of thecandidate banking syndicate identifier connected domain is not greaterthan the threshold value (e.g., Tsolid) of the specified identifierrange, the candidate domain area is discarded. In the event that thevolume ratio of the candidate banking syndicate identifier connecteddomain is greater than the threshold value (e.g., Tsolid), the candidateconnected domain is filled. The volume ratio threshold determiningsub-module can be implemented to perform 730 of process 700.

The filling computation sub-module is configured to compute the majoraxis direction of the circumscribed ellipse of the filled said candidateconnected domain area. The filling computation sub-module can determinethe direction of the filled banking syndicate identifier candidateconnected domain area. The filling computation sub-module can beimplemented to perform 740 and/or 750 of process 700.

The angle of intersection determining sub-module is configured todetermine whether the angle of intersection is within a predeterminedthreshold range. For example, the angle of intersection determiningsub-module is configured to determine whether the angle of intersectioncorresponding to the banking syndicate identifier candidate connecteddomain is within a predetermined threshold value of the angle ofintersection of the standard banking syndicate identifier image. In theevent that the angle of intersection corresponding to the bankingsyndicate identifier candidate connected domain is not within thepredetermined threshold value of the angle of intersection of thestandard banking syndicate identifier image, the candidate domain areais discarded. In the event that the angle of intersection correspondingto the banking syndicate identifier candidate connected domain is withinthe predetermined threshold value of the angle of intersection of thestandard banking syndicate identifier image, the banking syndicateidentifier candidate connected domain is determined to be (e.g.,regarded as) the area in which the banking syndicate identifier image islocated. The angle of intersection determining sub-module can implement760, 765, and/or 770 of process 700.

In some embodiments, the single image feature screening sub-module caninclude a filling sub-module, an edge contour retrieval sub-module, asampling sub-module, a sampling pixel point fitting sub-module, and asegment combination determining sub-module. In some embodiments, thesingle image feature screening sub-module is used to implement process800 of FIG. 8.

The filling sub-module is configured to fill in the candidate connecteddomain area to obtain a filled image. The filling sub-module canimplement 810 of process 800.

The edge contour retrieval sub-module is configured to obtain an edgecontour image. For example, the edge contour retrieval sub-module canobtain the edge contour image of the filled image based on the detectededges of said filled image. The edge contour retrieval sub-module canimplement 820 of process 800.

The sampling sub-module is configured to retrieve sampling pixel pointsat edges of the edge contour image. The sampling sub-module canseparately retrieve sampling pixel points for the top and bottom edgesand sampling pixel points for the left and right edges of said edgecontour image. The sampling sub-module can select the sampling pixelpoints for the top and bottom edges and sampling pixel points for theleft and right edges within a set threshold range. The samplingsub-module can implement 830 of process 800.

The sampling pixel point fitting sub-module is configured to obtain linesegment combination information of the edge contour image. The samplingpixel point fitting sub-module can separately retrieve sampling pixelpoints for the top and bottom edges and left and right edges of saidedge contour image, and select the sampling pixel points for the top andbottom edges and sampling pixel points for the left and right edgeswithin a set threshold range. The sampling pixel point fittingsub-module can implement 840 of process 800.

The segment combination determining sub-module is configured todetermine whether the segment combination information matches thesegment combination information of the standard specified identifierimage. In some embodiments, the segment combination determiningsub-module determines whether the segment combination information andintersection point location information are identical to the segmentcombination information and intersection point location information ofthe standard specified identifier image. The segment combinationdetermining sub-module implements 850, 855, and/or 860 of process 800.

In the event that the segment combination information does not match thesegment combination information of the standard specified identifierimage, the segment combination determining sub-module discards thecandidate domain area. In some embodiments, the segment combinationdetermining sub-module provides an indication that the specifiedidentifier image area has not been detected. The indication can indicatea failure of the detection of the specified identifier image area.

In the event that the segment combination information matches thesegment combination information of the standard specified identifierimage, the segment combination determining sub-module determines thatthe candidate domain area corresponds to the specified identifier image.

In some embodiments, the sampling sub-module includes a scanningsub-module and a distance threshold value determining sub-module.

The scanning sub-module of the sampling sub-module is configured to scanfor the presence of two points of intersection with the filled image asthe condition, and to use the identified intersection points as thesampling pixel points.

The distance threshold value determining sub-module of the samplingsub-module is configured to determine whether the distance between thetop and bottom edges and the distance between the left and right edgesof the retrieved sampling pixel points are within the set thresholdrange. If the distance between the top and bottom edges and the distancebetween the left and right edges of the retrieved sampling pixel pointsare within the set threshold range, then the pixel points are selected.If the distance between the top and bottom edges and the distancebetween the left and right edges of the retrieved sampling pixel pointsare not within the set threshold range, then pixel points are discarded.The distance threshold value determining sub-module uses the range ofthreshold values that can correspond to maxDist±maxDist×5%, wheremaxDist is the maximum distance between two sampling pixel points.

In some embodiments, device 1000 further comprises an affinetransformation module configured to compute the affine transformationmatrix based on the location information of the specified identifier inthe predetermined area or image including the predetermined area, andthe location information of each intersection point. In someembodiments, device 1000 further comprises a related informationretrieval module configured to retrieve related information based on theaffine transformation matrix.

The modules (or sub-modules) described above can be implemented assoftware components executing on one or more general purpose processors,as hardware such as programmable logic devices and/or ApplicationSpecific Integrated Circuits designed to perform certain functions or acombination thereof. In some embodiments, the modules can be embodied bya form of software products which can be stored in a nonvolatile storagemedium (such as optical disk, flash storage device, mobile hard disk,etc.), including a number of instructions for making a computer device(such as personal computers, servers, network equipment, etc.) implementthe methods described in the embodiments of the present invention. Themodules may be implemented on a single device or distributed acrossmultiple devices. The functions of the modules may be merged into oneanother or further split into multiple sub-modules.

FIG. 11 is a functional diagram of a computer system for detecting aspecified identifier according to various embodiments of the presentapplication.

Referring to FIG. 11, a computer system 1100 for detecting a specifiedidentifier is displayed. As will be apparent, other computer systemarchitectures and configurations can be used to detect a specifiedidentifier. Computer system 1100, which includes various subsystems asdescribed below, includes at least one microprocessor subsystem (alsoreferred to as a processor or a central processing unit (CPU)) 1102. Forexample, processor 1102 can be implemented by a single-chip processor orby multiple processors. In some embodiments, processor 1102 is a generalpurpose digital processor that controls the operation of the computersystem 1100. Using instructions retrieved from memory 1110, theprocessor 1102 controls the reception and manipulation of input data,and the output and display of data on output devices (e.g., display1118).

Processor 1102 is coupled bi-directionally with memory 1110, which caninclude a first primary storage, typically a random access memory (RAM),and a second primary storage area, typically a read-only memory (ROM).As is well known in the art, primary storage can be used as a generalstorage area and as scratch-pad memory, and can also be used to storeinput data and processed data. Primary storage can also storeprogramming instructions and data, in the form of data objects and textobjects, in addition to other data and instructions for processesoperating on processor 1102. Also as is well known in the art, primarystorage typically includes basic operating instructions, program code,data, and objects used by the processor 1102 to perform its functions(e.g., programmed instructions). For example, memory 1110 can includeany suitable computer-readable storage media, described below, dependingon whether, for example, data access needs to be bi-directional oruni-directional. For example, processor 1102 can also directly and veryrapidly retrieve and store frequently needed data in a cache memory (notshown). The memory can be a non-transitory computer-readable storagemedium.

A removable mass storage device 1112 provides additional data storagecapacity for the computer system 1100, and is coupled eitherbi-directionally (read/write) or uni-directionally (read only) toprocessor 1102. For example, storage 1112 can also includecomputer-readable media such as magnetic tape, flash memory, PC-CARDS,portable mass storage devices, holographic storage devices, and otherstorage devices. A fixed mass storage 1120 can also, for example,provide additional data storage capacity. The most common example ofmass storage 1120 is a hard disk drive. Mass storage device 1112 andfixed mass storage 1120 generally store additional programminginstructions, data, and the like that typically are not in active use bythe processor 1102. It will be appreciated that the information retainedwithin mass storage device 1112 and fixed mass storage 1120 can beincorporated, if needed, in standard fashion as part of memory 1110(e.g., RAM) as virtual memory.

In addition to providing processor 1102 access to storage subsystems,bus 1114 can also be used to provide access to other subsystems anddevices. As shown, these can include a display monitor 1118, a networkinterface 1116, a keyboard 1104, and a pointing device 1106, as well asan auxiliary input/output device interface, a sound card, speakers, andother subsystems as needed. For example, the pointing device 1106 can bea mouse, stylus, track ball, or tablet, and is useful for interactingwith a graphical user interface.

The network interface 1116 allows processor 1102 to be coupled toanother computer, computer network, or telecommunications network usinga network connection as shown. For example, through the networkinterface 1116, the processor 1102 can receive information (e.g., dataobjects or program instructions) from another network or outputinformation to another network in the course of performingmethod/process steps. Information, often represented as a sequence ofinstructions to be executed on a processor, can be received from andoutputted to another network. An interface card or similar device andappropriate software implemented by (e.g., executed/performed on)processor 1102 can be used to connect the computer system 1100 to anexternal network and transfer data according to standard protocols. Forexample, various process embodiments disclosed herein can be executed onprocessor 1102, or can be performed across a network such as theInternet, intranet networks, or local area networks, in conjunction witha remote processor that shares a portion of the processing. Additionalmass storage devices (not shown) can also be connected to processor 1102through network interface 1116.

An auxiliary I/O device interface (not shown) can be used in conjunctionwith computer system 1100. The auxiliary I/O device interface caninclude general and customized interfaces that allow the processor 1102to send and, more typically, receive data from other devices such asmicrophones, touch-sensitive displays, transducer card readers, tapereaders, voice or handwriting recognizers, biometrics readers, cameras,portable mass storage devices, and other computers.

The computer system shown in FIG. 11 is but an example of a computersystem suitable for use with the various embodiments disclosed herein.Other computer systems suitable for such use can include additional orfewer subsystems. In addition, bus 1114 is illustrative of anyinterconnection scheme serving to link the subsystems. Other computerarchitectures having different configurations of subsystems can also beutilized.

The methods or algorithmic steps described in light of the embodimentsdisclosed herein can be implemented using hardware, processor-executedsoftware modules, or combinations of both. Software modules can beinstalled in random-access memory (RAM), memory, read-only memory (ROM),electrically programmable ROM, electrically erasable programmable ROM,registers, hard drives, removable disks, CD-ROM, or any other forms ofstorage media known in the technical field.

In one typical configuration, computer equipment comprises one or moreprocessors (CPUs), input/output interfaces, network interfaces, andinternal memory.

Internal memory may include non-permanent memory, random access memory(RAM) and/or non-volatile memory contained in computer-readable media,such as read-only memory (ROM) or flash memory (flash RAM). Internalmemory is an example of a computer-readable medium.

Computer-readable media include permanent, non-permanent, mobile, andnon-mobile media that can be used to store information by any method ortechnology. The information can be computer-readable commands, datastructures, program modules, or other data. Examples of computer storagemedia include but are not limited to phase-change memory (PRAM), staticrandom access memory (SRAM), dynamic random access memory (DRAM), othertypes of random access memory (RAM), read-only memory (ROM),electrically erasable programmable read-only memory (EEPROM), flashmemory or other memory technology, compact disk read-only memory(CD-ROM), digit multifunction disc (DVD) or other optical storage,magnetic cassettes, magnetic tape or magnetic disc storage, or othermagnetic storage equipment or any other non-transmission media that canbe used to store information that is accessible to computers. As definedin this document, computer-readable media does not include temporarycomputer-readable media (transitory media), such as modulated datasignals and carrier waves.

Although the foregoing embodiments have been described in some detailfor purposes of clarity of understanding, the invention is not limitedto the details provided. There are many alternative ways of implementingthe invention. The disclosed embodiments are illustrative and notrestrictive.

What is claimed is:
 1. A method, comprising: obtaining, by one or moreprocessors, a target image; determining a plurality of areas of arepresentation of the target image; obtaining, by the one or moreprocessors, a set of one or more image features of one or more of theplurality of areas of the representation of the target image; comparingat least one of the set of one or more image features to image featuresof a standard identifier image; and determining, by the one or moreprocessors, at least one of the plurality of areas of the target imageas a location from which data is to be obtained and used in connectionwith an authentication, the location being determined based at least inpart on a result of the comparing of the at least one of the set of oneor more image features to the image features of the standard identifierimage.
 2. The method of claim 1, wherein the representation of thetarget image corresponds to a is target binary image that is obtainedbased at least in part on a binarizing of the target image.
 3. Themethod of claim 1, wherein the representation of the target imagecorresponds to an image that is obtained based at least in part on aprocessing of the target image.
 4. The method of claim 1, furthercomprising: scanning the location from which data is to be obtained andused in connection with an authentication, the location being scannedwith respect to the target image or the representation of the targetimage; obtaining the data from the location; detecting one or morefeatures in the location based at least in part on the data obtainedfrom the location; and authenticating the target image or a physicalcard corresponding to the target image based at least in part on the oneor more features being detected in the location.
 5. The method of claim1, wherein the representation of the target image corresponds to abinary image of the target image and a negative image of the binaryimage.
 6. The method of claim 1, wherein the determining the pluralityof areas of the target image comprises calibrating the plurality ofareas of the representation of the target image.
 7. The method of claim1, wherein the plurality of areas of the target image correspond toconnected domains that have a number of pixels that satisfy a thresholdvalue.
 8. The method of claim 1, wherein the location from which data isto be obtained and used in connection with the authenticationcorresponds to a location comprising at least part of the standardidentifier image.
 9. The method of claim 8, wherein the at least one ofthe plurality of areas of the target image determined as the locationcorresponds to a domain having a value of a similarity relative to thestandard identifier image that is equal to or greater than a presetsimilarity threshold.
 10. The method of claim 1, wherein obtaining thetarget image comprises: retrieving a preview image of an imageassociated with a predetermined area; determining an area range of thepreview image; framing the preview image within the area range; anddetermining the preview image framed within the area range to be thetarget image.
 11. The method of claim 1, wherein obtaining the targetimage comprises: intercepting an area range including the standardidentifier image to serve as the target image based at least in part onspecified location information of the standard identifier image in apredetermined area or predetermined area image.
 12. The method of claim11, wherein the specified location information corresponds to a positionat which the standard specified identifier image is located in thepredetermined area or predetermined area image, and the position atwhich the standard identifier image is located in the predetermined areais set according to one or more standard specifications associated withthe standard specified identifier image.
 13. The method of claim 11,wherein the intercepting of the area range including the standardidentifier image to serve as the target image comprises: establishing astandard image template of the predetermined area or image including thepredetermined area; traversing the standard image template, andobtaining location information associated with the standard identifierimage in the predetermined area or image including the predeterminedarea based on correspondences between the standard image template andthe predetermined area or image including the predetermined area; anddetermining the target image based on obtained location information. 14.The method of claim 1, wherein the plurality of areas of the targetimage correspond to connected domains that have a number of pixels thatsatisfy a threshold value, an upper limit value of the threshold valuecorresponds to a product of multiplying a width, a height, and an upperlimit coefficient of the retrieved target image, and wherein a lowerlimit value of the threshold value corresponds to a product ofmultiplying the width, the height, and a lower limit coefficient of theretrieved target image.
 15. The method of claim 1, wherein the standardidentifier image corresponds to a logo graphic of a banking syndicate orbank card issuer.
 16. The method of claim 1, wherein the obtaining thetarget image comprises capturing an image of an object using a cameraconnected to a device, and extracting the target image from the image ofthe object using one or more processors.
 17. The method of claim 1,wherein the obtaining the target image comprises connecting to one ormore servers via a network, and receiving the target image from the oneor more servers, wherein the target image is extracted from an image ofan object that was captured using a camera connected to a device andthat was communicated to the one or more services by the device.
 18. Themethod of claim 1, further comprising: determining, by the one or moreprocessors, whether the set of one or more image features of one or moreof the plurality of areas of the target image match corresponding one ormore standard image features in the standard identifier image, whereinthe target image is extracted from an image of an object that wascaptured using a camera connected to a device; and in response to adetermination that the set of one or more image features of one or moreof the plurality of areas of the target binary image are determined tomatch the corresponding one or more standard image features in thestandard identifier image, deeming the object that was captured usingthe camera to be authentic.
 19. The method of claim 1, furthercomprising: determining, by the one or more processors, whether the setof one or more image features of one or more of the plurality of areasof the target image match corresponding one or more standard imagefeatures in the standard identifier image, wherein the target image isextracted from an image of an object that was captured using a cameraconnected to a device; and in response to a determination that the setof one or more image features of one or more of the plurality of areasof the target binary image are determined to match the corresponding oneor more standard image features in the standard identifier image, usingthe object that was captured using the camera in connection withauthenticating a user associated with the object.
 20. A device,comprising: at least one processor configured to: obtain, by one or moreprocessors, a target image; determine a plurality of areas of arepresentation of the target image; is obtain, by the one or moreprocessors, a set of one or more image features of one or more of theplurality of areas of the representation of the target image; compare atleast one of the set of one or more image features to image features ofa standard identifier image; and determine, by the one or moreprocessors, at least one of the plurality of areas of the target imageas a location from which data is to be obtained and used in connectionwith an authentication, the location being determined based at least inpart on a result of the comparing of the at least one of the set of oneor more image features to the image features of the standard identifierimage; and a memory coupled to the at least one processor and configuredto provide the at least one processor with instructions.
 21. A computerprogram product, the computer program product being embodied in anon-transitory computer readable storage medium and comprising computerinstructions for: obtaining, by one or more processors, a target image;determining a plurality of areas of a representation of the targetimage; obtaining, by the one or more processors, a set of one or moreimage features of one or more of the plurality of areas of therepresentation of the target image; comparing at least one of the set ofone or more image features to image features of a standard identifierimage; and determining, by the one or more processors, at least one ofthe plurality of areas of the target image as a location from which datais to be obtained and used in connection with an authentication, thelocation being determined based at least in part on a result of thecomparing of the at least one of the set of one or more image featuresto the image features of the standard identifier image.